LangGraph Cloud Quick Start

In this tutorial you will build and deploy a simple chatbot agent that can look things up on the internet. You will be using LangGraph Cloud, LangGraph Studio to visualize and test it out, and LangGraph SDK to interact with the deployed agent.

If you want to learn how to build an agent like this from scratch, take a look at the LangGraph Quick Start tutorial.

Set up requirements

This tutorial will use:

• Anthropic for the LLM - sign up and get an API key here
• Tavily for the search engine - sign up and get an API key here
• LangSmith for hosting - sign up and get an API key here

Create and configure your app

First, let's set create all of the necessary files for our LangGraph application.

1. Create application directory and files

   Create a new application my-app with the following file structure:

   mkdir my-app
   

   PythonJavascript

   my-app/
   |-- agent.py            # code for your LangGraph agent
   |-- requirements.txt    # Python packages required for your graph
   |-- langgraph.json      # configuration file for LangGraph
   |-- .env                # environment files with API keys
   

   my-app/
   |-- agent.ts            # code for your LangGraph agent
   |-- package.json        # Javascript packages required for your graph
   |-- langgraph.json      # configuration file for LangGraph
   |-- .env                # environment files with API keys
   

2. Define your graph

   PythonJavascript

   The agent.py file should contain code with your graph.

   The agent.ts file should contain code with your graph.

   The following code example is a simple chatbot agent (similar to the one in the previous tutorial). Specifically, it uses create_react_agent, a prebuilt ReAct-style agent.

   The agent file needs to have a variable with a CompiledGraph (in this case the graph variable).

   PythonJavascript

   # agent.py
   from langchain_anthropic import ChatAnthropic
   from langchain_community.tools.tavily_search import TavilySearchResults
   from langgraph.prebuilt import create_react_agent
   
   model = ChatAnthropic(model="claude-3-5-sonnet-20240620")
   
   tools = [TavilySearchResults(max_results=2)]
   
   # compiled graph
   graph = create_react_agent(model, tools)
   

   // agent.ts
   import { ChatAnthropic } from "@langchain/anthropic";
   import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
   import { createReactAgent } from "@langchain/langgraph/prebuilt";
   
   const model = new ChatAnthropic({
     model: "claude-3-5-sonnet-20240620",
   });
   
   const tools = [
     new TavilySearchResults({ maxResults: 3, }),
   ];
   
   // compiled graph
   export const graph = createReactAgent({ llm: model, tools });
   

3. Specify dependencies

   PythonJavascript

   You should add dependencies for your graph(s) to requirements.txt.

   You should add dependencies for your graph(s) to package.json.

   In this case we only require four packages for our graph to run:

   PythonJavascript

   langgraph
   langchain_anthropic
   tavily-python
   langchain_community
   

   {
     "name": "my-app",
     "packageManager": "yarn@1.22.22",
     "dependencies": {
       "@langchain/community": "^0.3.11",
       "@langchain/core": "^0.3.16",
       "@langchain/langgraph": "0.2.18",
       "@langchain/anthropic": "^0.3.7"
     }
   }
   

4. Create LangGraph configuration file

   The langgraph.json file is a configuration file that describes what graph(s) you are going to deploy. In this case we only have one graph: the compiled graph object from agent.py / agent.ts.

   PythonJavascript

   {
     "dependencies": ["."],
     "graphs": {
       "agent": "./agent.py:graph"
     },
     "env": ".env"
   }
   

   {
     "node_version": "20",
     "dockerfile_lines": [],
     "dependencies": ["."],
     "graphs": {
       "agent": "./src/agent.ts:graph"
     },
     "env": ".env"
   }
   

   Learn more about the LangGraph CLI configuration file here.

5. Specify environment variables

   The .env file should have any environment variables needed to run your graph. This will only be used for local testing, so if you are not testing locally you can skip this step.

   Warning

   The .env file should NOT be included with the rest of source code in your Github repository. When creating a deployment using LangGraph Cloud, you will be able to specify the environment variables manually.

   For this graph, we need two environment variables:

   ANTHROPIC_API_KEY=...
   TAVILY_API_KEY=...
   

Tip

Learn more about different application structure options here.

Now that we have set everything up on our local file system, we are ready to test our graph locally.

Test the app locally

To test the LangGraph app before deploying it using LangGraph Cloud, you can start the LangGraph server locally or use LangGraph Studio.

Using local server

You can test your app by running LangGraph server locally. This is useful to make sure you have configured our CLI configuration file correctly and can interact with your graph.

To run the server locally, you need to first install the LangGraph CLI:

pip install langgraph-cli

You can then test our API server locally. In order to run the server locally, you will need to add your LANGSMITH_API_KEY to the .env file.

langgraph up

This will start up the LangGraph API server locally. If this runs successfully, you should see something like:

Ready!
- API: http://localhost:8123

First, let's verify that the server is running correctly by calling /ok endpoint:

curl --request GET --url http://localhost:8123/ok

Output:

{"ok": "true"}

Now we're ready to test the app with the real inputs!

curl --request POST \
    --url http://localhost:8123/runs/stream \
    --header 'Content-Type: application/json' \
    --data '{
    "assistant_id": "agent",
    "input": {
        "messages": [
            {
                "role": "user",
                "content": "What is the weather in NYC?"
            }
        ]
    },
    "stream_mode": "updates"
}'

Output:

...

data: {
  "agent": {
    "messages": [
      {
        "content": "The search results from Tavily provide the current weather conditions in New York City, including temperature, wind speed, precipitation, humidity, and cloud cover. According to the results, as of 3:00pm on October 30th, 2024, it is overcast in NYC with a temperature of around 66°F (19°C), light winds from the southwest around 8 mph (13 km/h), and 66% humidity.\n\nSo in summary, the current weather in NYC is overcast with mild temperatures in the mid 60sF and light winds, based on the search results. Let me know if you need any other details!",
        "type": "ai",
        ...
      }
    ]
  }
}

You can see that our agent responds with the up-to-date search results!

Using LangGraph Studio Desktop

You can also test your app locally with LangGraph Studio. LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications.

With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith allowing you to collaborate with teammates to debug failure modes.

LangGraph Studio is available as a desktop app for MacOS users. Once you have installed the app, you can select my-app directory, which will automatically start the server locally and load the graph in the UI.

To interact with your chatbot agent in LangGraph Studio, you can add a new message in the Input section and press Submit.

Deploy to LangGraph Cloud

Once you've tested your graph locally and verified that it works as expected, you can deploy it to the LangGraph Cloud.

First, you'll need to turn the my-app directory into a GitHub repo and push it to GitHub.

Once you have created your GitHub repository with a Python file containing your compiled graph as well as a langgraph.json with the configuration, you can head over to LangSmith and click on the graph icon (LangGraph Cloud) on the bottom of the left navbar. This will open the LangGraph deployments page. On this page, click the + New Deployment button in the top right corner.

If you have not deployed to LangGraph Cloud before: there will be a button that shows up saying Import from GitHub. You’ll need to follow that flow to connect LangGraph Cloud to GitHub.

Once you have set up your GitHub connection: the new deployment page will look as follows:

To deploy your application, you should do the following:

1. Select your GitHub username or organization from the selector
2. Search for your repo to deploy in the search bar and select it
3. Choose a name for your deployment
4. In the Git Branch field, you can specify either the branch for the code you want to deploy, or the exact commit SHA.
5. In the LangGraph API config file field, enter the path to your langgraph.json file (which in this case is just langgraph.json)
6. If your application needs environment variables, add those in the Environment Variables section. They will be propagated to the underlying server so your code can access them. In this case, we will need ANTHROPIC_API_KEY and TAVILY_API_KEY.

Hit Submit and your application will start deploying!

After your deployment is complete, your deployments page should look as follows:

Interact with your deployment

Using LangGraph Studio (Cloud)

On the deployment page for your application,, you should see a button in the top right corner that says LangGraph Studio. Clicking on this button will take you to the web version of LangGraph Studio. This is the same UI that you interacted with when testing the app locally, but instead of using a local LangGraph server, it uses the one from your LangGraph Cloud deployment.

Using LangGraph SDK

You can also interact with your deployed LangGraph application programmatically, using LangGraph SDK.

First, make sure you have the SDK installed:

PythonJavascript

pip install langgraph_sdk

yarn add @langchain/langgraph-sdk

Before using, you need to get the URL of your LangGraph deployment. You can find this in the Deployment view. Click the URL to copy it to the clipboard.

You also need to make sure you have set up your API key properly so you can authenticate with LangGraph Cloud.

export LANGSMITH_API_KEY=...

The first thing to do when using the SDK is to setup our client, access our assistant, and create a thread to execute a run on:

PythonJavascriptCURL

from langgraph_sdk import get_client

client = get_client(url=<DEPLOYMENT_URL>)
# get default assistant
assistants = await client.assistants.search(metadata={"created_by": "system"})
assistant = assistants[0]
# create thread
thread = await client.threads.create()
print(thread)

import { Client } from "@langchain/langgraph-sdk";

const client = new Client({ apiUrl: <DEPLOYMENT_URL> });
// get default assistant
const assistants = await client.assistants.search({ metadata: {"created_by": "system"} })
const assistant = assistants[0];
// create thread
const thread = await client.threads.create();
console.log(thread)

curl --request POST \
    --url <DEPLOYMENT_URL>/assistants/search \
    --header 'Content-Type: application/json' \
    --data '{
        "limit": 10,
        "offset": 0,
        "metadata": {"created_by": "system"}
    }' &&
curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'

We can then execute a run on the thread:

PythonJavascriptCURL

input = {
    "messages": [{"role": "user", "content": "What is the weather in NYC?"}]
}

async for chunk in client.runs.stream(
    thread["thread_id"],
    assistant["assistant_id"],
    input=input,
    stream_mode="updates",
):
    if chunk.data:
        print(chunk.data)

const input = { "messages": [{ "role": "user", "content": "What is the weather in NYC?" }] };

const streamResponse = client.runs.stream(
  thread["thread_id"],
  assistant["assistant_id"],
  {
    input,
    streamMode: "updates"
  }
);
for await (const chunk of streamResponse) {
  if (chunk.data) {
    console.log(chunk.data);
  }
}

curl --request POST \
  --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream \
  --header 'Content-Type: application/json' \
  --data '{
    "assistant_id": <ASSISTANT_ID>,
    "input": {
    "messages": [
        {
            "role": "user",
            "content": "What is the weather in NYC?"
        }
      ]
    },
    "stream_mode": "updates"
  }'

Output:

...

data: {
  "agent": {
    "messages": [
      {
        "content": "The search results from Tavily provide the current weather conditions in New York City, including temperature, wind speed, precipitation, humidity, and cloud cover. According to the results, as of 3:00pm on October 30th, 2024, it is overcast in NYC with a temperature of around 66°F (19°C), light winds from the southwest around 8 mph (13 km/h), and 66% humidity.\n\nSo in summary, the current weather in NYC is overcast with mild temperatures in the mid 60sF and light winds, based on the search results. Let me know if you need any other details!",
        "type": "ai",
        ...
      }
    ]
  }
}

Next steps

Congratulations! If you've worked your way through this tutorial you are well on your way to becoming a LangGraph Cloud expert. Here are some other resources to check out to help you out on the path to expertise:

• LangGraph How-to guides
• LangGraph Tutorials

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